What is wrong with my lsqcurvefit script here?

I have Xdata and Ydata as input data points. And I need to do non-linear regression. Here's what I am doing.

      K=2; V=0.3;
      X0=[K,V];
  options = optimset('DiffMinChange',[0.000001],'disp','iter','Algorithm',[],'MaxIter',100000,'MaxFunEvals',[10000]);%simple max eval fun 100000
  options = optimset(options, 'TolX', 1e-14);
      [fitted_param] = lsqcurvefit(@(fitted_param,XDATA) ((1+(XDATA*K)).*exp(V*XDATA)),X0,XDATA,YDATA,[],[],options)

It stops at one step and give the same values of parameters as I provide as input.

Thanks

Answer 1

[Walter Roberson]

      [fitted_param] = lsqcurvefit(@(fitted_param,XDATA) ((1+(XDATA*K)).*exp(V*XDATA)),X0,XDATA,YDATA,[],[],options)

Your target function @(fitted_param,XDATA) ((1+(XDATA*K)).*exp(V*XDATA)) ignores the first parameter, returning the same output no matter what model parameters are suggested in its first input. lsqcurvefit determines that the output is not changing with the input model parameters and so figures the the initial model parameters X0 are as good as any other possibilities.

I suspect you are looking for something like

[fitted_param] = lsqcurvefit(@(KV,XDATA) ((1+(XDATA*KV(1))).*exp(KV(2)*XDATA)), X0, XDATA, YDATA, [], [], options)